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Edge Computing with Big data Cloud Architecture: A Case Study in Smart Building

Edge Computing with Big Data Cloud Architecture: A Case Stud...

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IEEE international conference on Big data

作者： Catherine Inibhunu Carolyn McGregor Ontario Tech University Oshawa Ontario Canada University Technology Sydney Ultimo NSW Australia

ISBN: (数字)9781728162515

ISBN: (纸本)9781728162522

The growth of buildings embedded with technologies that can monitor the internal building environment with respect to energy consumption such as heating, ventilation, air conditioning, wind, motion as well occupancy have an immense potential. From energy management, occupancy administration, security maintenance as well as improving the health and quality of life for humans in indoor or outdoor spaces. These potentials can be realized by a clear understanding of the interplay between vast environmental conditions, humans and their health as well as the many smart products they interact with in their lives. This is a complex process that requires thorough testing and evaluation within smart buildings simulation environments where multiple buildings data can be generated and then effectively analyzed. This can be facilitated by a robust data management process that utilizes big data computing technologies to harness large volumes, variety and velocity of data that can be captured within smart buildings while maintain the security and privacy of data *** this paper we describe a smart building architecture that has been designed and developed for management of data from a smart building. In particular the architecture enables acquisition, processing and distribution of simulated environmental building data to multiple consumers and workflows for further processing and analysis locally and in a high performance cloud computing platform. The research premise is that such an architecture enables effective management of multiple data sources within climatic based simulated testing in smart buildings to further research.

关键词： Cloud computing Smart buildings Architecture Computer architecture Big data data models Testing

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HBD-Authority: Streaming Access Control Model for Hadoop

HBD-Authority: Streaming Access Control Model for Hadoop

引用

Dependability in Sensor, Cloud and Big data Systems and Application (DependSys), IEEE international conference on

作者： Congwei Chen Marwa A. Elsayed Mohammad Zulkernine School of Computing Queen's University Kingston ON Canada

ISBN: (数字)9781728176512

ISBN: (纸本)9781728176529

Big data analytics, in essence, is becoming the revolution of business intelligence around the world. This momentum has given rise to the hype around analytic technologies, including Apache Hadoop. Hadoop was not originally developed with security in mind. Despite the evolving efforts to integrate security in Hadoop through developing new tools (e.g., Apache Sentry and Ranger) and employing traditional mechanisms (e.g., Kerberos and LDAP), they mainly focus on providing encryption and authentication features, albeit with limited authorization support. Existing solutions in the literature extended these evolving efforts. However, they suffer from limitations, hindering them from providing robust authorization that effectively meets the unique requirements of big data environments. Towards covering this gap, this paper proposes a hybrid authority (HBD-Authority) as a formal attribute-based access control model with context support. This model is established on a novel hybrid approach of authorization transparency that pertains to three fundamental properties of accuracy: correctness, security, and completeness. The model leverages streaming data analytics to foster distributed parallel processing capabilities that achieve multifold benefits: a) efficiently managing the security policies and promptly updating the privileges assigned to a high number of users interacting with the analytic services; b) swiftly deciding and enforcing authorization of requests over data characterized by the 5Vs; and c) providing dynamic protection for data which is frequently updated. The implementation details and experimental evaluation of the proposed model are presented, demonstrating its performance efficiency.

关键词： Authorization Analytical models Distributed databases Big data Parallel processing data models Security

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A trajectory simplification method based on the clustering of vessel speed and course segments

A trajectory simplification method based on the clustering o...

引用

international conference on Informative and Cybernetics for Computational Social Systems (ICCSS)

作者： Jia Li Yongjie Zhu Yi Zuo Tieshan Li Navigation College Dalian Maritime University Dalian China

ISBN: (数字)9781728162461

ISBN: (纸本)9781665419352

Automatic Identification Systems (AIS) can provide massive ship trajectory data that is valuable for mining information in water traffic. However, it is also easy to cause the data redundancy, adding difficulty to the data processing. To address this problem, this paper proposes a trajectory simplification method based on the segmental clustering of the ship's speed and course. Firstly, the maneuver points of a trajectory are determined by the speed and course threshold. The trajectory is divided into segments in accordance with the maneuver points. Then, the feature points of each segment are extracted based on K-medoids clustering method. The maneuver points and feature points of every trajectory segments are combined as trajectory features s to achieve the purpose of simplifying the ship's trajectory. The experiment results show that the method can well maintain the feature of original trajectory and eliminate the redundant track points in AIS trajectory. Compared with tradition Douglas-Peuker (DP) algorithm, the distortion rate could be reduced 0.2% with the same simplification.

关键词： Clustering algorithms Rate distortion theory Feature extraction Distortion Trajectory data mining Marine vehicles

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A Machine Learning Framework for Prevention of Software-Defined Networking controller from DDoS Attacks and dimensionality reduction of big data

A Machine Learning Framework for Prevention of Software-Defi...

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international conference on Information and Communication Technology Convergence (ICTC)

作者： Jehad Ali Byeong-hee Roh Byungkyu Lee Jimyung Oh Muhammad Adil Ajou University Suwon-si South Korea LIG Nex1 Co. Ltd. Seongnam South Korea Virtual University Lahore Pakistan

ISBN: (数字)9781728167589

ISBN: (纸本)9781728199016

The controller is an indispensable entity in software-defined networking (SDN), as it maintains a global view of the underlying network. However, if the controller fails to respond to the network due to a distributed denial of service (DDoS) attacks. Then, the attacker takes charge of the whole network via launching a spoof controller and can also modify the flow tables. Hence, faster, and accurate detection of DDoS attacks against the controller will make the SDN reliable and secure. Moreover, the Internet traffic is drastically increasing due to unprecedented growth of connected devices. Consequently, the processing of large number of requests cause a performance bottleneck regarding SDN controller. In this paper, we propose a hierarchical control plane SDN architecture for multi-domain communication that uses a statistical method called principal component analysis (PCA) to reduce the dimensionality of the big data traffic and the support vector machine (SVM) classifier is employed to detect a DDoS attack. SVM has high accuracy and less false positive rate while the PCA filters attribute drastically. Consequently, the performance of classification and accuracy is improved while the false positive rate is reduced.

关键词： Support vector machines Dimensionality reduction Big data Denial-of-service attack Prediction algorithms Classification algorithms Principal component analysis

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A Study on Machine Learning based Conversational Agents and Designing Techniques

A Study on Machine Learning based Conversational Agents and ...

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international conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)

作者： Santhana Lakshmi V. PSGR Krishnammal College for Women Coimbatore India

ISBN: (数字)9781728154640

ISBN: (纸本)9781728154657

Chatbots are a computer program that was created to imitate the human during a conversation. In this technological era, humans were replaced by machines for performing most of the work. So chatbots were developed to mimic the conversation a human does with another person. The work a chatbot does ranges from answering simple queries to acting as personal assistant to the boss. There are different kinds of chatbots developed to cater to the needs of the people in different domain. The methodology of creating them also varies depending on their type. In this paper, the various types of chatbots and techniques such as Machine Learning, deep learning and natural language processing used for designing them were discussed in detail.

关键词： Deep learning Chatbot data models

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An Efficient GPU Framework for Parallelizing Combinatorial Optimization Heuristics

An Efficient GPU Framework for Parallelizing Combinatorial O...

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international conference on Advances in Computing and Communication Engineering (ICACCE)

作者： Mohammad Harun Rashid Ian McAndrew Capitol Technology University USA

ISBN: (数字)9781728163628

ISBN: (纸本)9781728163635

Combinatorial optimization problems are often NP-hard and too complex to be solved within a reasonable time frame by exact methods. Heuristic methods, which do not offer a convergence guarantee could obtain some satisfactory resolution for combinatorial optimization problems. However, it is not only very time-consuming for Central processing Units (CPU) but also very difficult to obtain an optimized solution when solving large problem instances. So, parallelism can be a good technique for reducing the time complexity, as well as improving the solution quality. Nowadays Graphics processing Units (GPUs) have evolved supporting general purpose computing. GPUs have become many core processors, multithreaded, highly parallel with high bandwidth memory and tremendous computational power due to the market demand for high definition and real time 3D graphics. Compared to CPU threads, GPU threads are very lightweight which means that changing the context between two threads is not a costly operation. So, GPU cores provide a low-cost opportunity to parallelizing metaheuristics for combinatorial optimization problems. The major issues for GPU parallelization are: 1) efficient distribution of data processing between GPU and CPU, 2) efficient parallelism control for the thread synchronization, 3) efficient memory management for the optimization of data transfer between the different memories, the capacity constraints of these memories, etc. Our proposed work aims to designing an efficient GPU framework which can efficiently deal with these important parallelization issues with GPU while parallelizing heuristics methods like hill climbing, simulated annealing algorithm etc. for solving large optimization problems.

关键词： Graphics processing units Optimization Memory management Instruction sets Central processing Unit data transfer Partitioning algorithms

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Unfairness Discovery and Prevention For Few-Shot Regression

Unfairness Discovery and Prevention For Few-Shot Regression

引用

IEEE international conference on Big Knowledge (ICBK)

作者： Chen Zhao Feng Chen Department of Computer Science The University of Texas at Dallas Richardson Texas USA

ISBN: (数字)9781728181561

ISBN: (纸本)9781728181578

We study fairness in supervised few-shot meta-learning models that are sensitive to discrimination (or bias) in historical data. A machine learning model trained based on biased data tends to make unfair predictions for users from minority groups. Although this problem has been studied before, existing methods mainly aim to detect and control the dependency effect of the protected variables (e.g. race, gender) on target prediction based on a large amount of training data. These approaches carry two major drawbacks that (1) lacking showing a global cause-effect visualization for all variables; (2) lacking generalization of both accuracy and fairness to unseen tasks. In this work, we first discover discrimination from data using a causal Bayesian knowledge graph which not only demonstrates the dependency of the protected variable on target but also indicates causal effects between all variables. Next, we develop a novel algorithm based on risk difference in order to quantify the discriminatory influence for each protected variable in the graph. Furthermore, to protect prediction from unfairness, a fast-adapted bias-control approach in meta-learning is proposed, which efficiently mitigates statistical disparity for each task and it thus ensures independence of protected attributes on predictions based on biased and few-shot data samples. Distinct from existing meta-learning models, group unfairness of tasks are efficiently reduced by leveraging the mean difference between (un)protected groups for regression problems. Through extensive experiments on both synthetic and real-world data sets, we demonstrate that our proposed unfairness discovery and prevention approaches efficiently detect discrimination and mitigate biases on model output as well as generalize both accuracy and fairness to unseen tasks with a limited amount of training samples.

关键词： Task analysis data models Predictive models data mining Bayes methods Machine learning Machine learning algorithms

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A Study of Deep Learning for Factoid Question Answering System

A Study of Deep Learning for Factoid Question Answering Syst...

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IEEE international conference on Information Reuse and Integration (IRI)

作者： Min-Yuh Day Yu-Ling Kuo Institute of Information Management National Taipei University Taipei Taiwan Department of Information Management Tamkang University New Taipei City Taiwan

ISBN: (数字)9781728110547

ISBN: (纸本)9781728110554

End-to-end question answering system has attracted considerable attention in the artificial intelligence research community in recent years. In this paper, we proposed an integrated deep learning model for factoid question answering system. This study uses the Delta Reading Comprehension dataset (DRCD) to build a model to implement a factoid question answering system and to combine the classification of question and answer to evaluate with exact match (EM) and F1 score. The study determines whether the comparison can increase the proportion of EM and whether the expected answer type can effectively increase the answer accuracy rate. To perfect the transformation, a question-and-answer system that uses the BERT pre-training model is applied to the DRCD dataset together with the expected answer type analysis and comparison. The contribution of this paper is that we proposed a system architecture of factoid question answering (QA) system using BERT with question expected answer type (Q-EAT) and answer type classification (AT) models. Findings confirm that the classification of question and answer can improve the EM ratio. When the question sentence and the answer classification are the same, the prediction accuracy EM of the question answering system will be improved.

关键词： Knowledge discovery Bit error rate Predictive models Machine learning Natural language processing data models Numerical models

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Where and When: Event-Based Spatiotemporal Trajectory Prediction from the iCub’s Point-Of-View

Where and When: Event-Based Spatiotemporal Trajectory Predic...

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IEEE international conference on Robotics and Automation (ICRA)

作者： Marco Monforte Ander Arriandiaga Arren Glover Chiara Bartolozzi Istituto Italiano di Tecnologia Università degli Studi di Genova and EDPR Research Line Italy

ISBN: (数字)9781728173955

ISBN: (纸本)9781728173962

Fast, non-linear trajectories have been shown to be more accurately visually measured, and hence predicted, when sampled spatially (that is when the target position changes) rather than temporally, i.e. at a fixed-rate as in traditional frame-based cameras. Event-cameras, with their asynchronous, low latency information stream, allow for spatial sampling with very high temporal resolution, improving the quality of the data and the accuracy of post-processing operations. This paper investigates the use of Long Short-Term Memory (LSTM) networks with event-cameras spatial sampling for trajectory prediction. We show the benefit of using an Encoder-Decoder architecture over parameterised models for regression on event-based human-to-robot handover trajectories. In particular, we exploit the temporal information associated to the events stream to predict not only the incoming spatial trajectory points, but also when these will occur in time. After having studied the proper LSTM input/output sequence length, the network performance are compared to other regression models. Then, prediction behavior and computational time are analysed for the proposed method. We carry out the experiment using an iCub robot equipped with event-cameras, addressing the problem from the robot perspective.

关键词： Trajectory Robots Predictive models Cameras Target tracking data models Pipelines

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Motor data analysis of Parkinson’s disease patients

Motor data analysis of Parkinson’s disease patients

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IEEE Symposium on Bioinformatics and Bioengineering (BIBE)

作者： Vasiliki Fiska Nikolaos Giannakeas Nikolaos Katertsidis Alexandros T. Tzallas Konstantinos Kalafatakis Markos G. Tsipouras Univ. of Western Macedonia Kozani Greece Science and Technology Park of Epirus UNIVEYE IKE Ioannina Greece Univ. of Ioannina Arta Greece

ISBN: (数字)9781728195742

ISBN: (纸本)9781728195759

In this manuscript, a methodology for analysing motor signals from Parkinson's disease (PD) patients is presented. The signals are obtained from PD patients while wearing a glove device and sequentially performing standard motor tests. The signals are processed in order to detect the onset and offset from specific items (items 23-25) of the Unified Parkinson's Disease Rating Scale (UPDRS) and then the isolated signal parts are analysed in order to quantity the motor findings defined in UPDRS for these items, such as hesitation, movement amplitude and frequency, and rotation range. The obtained results indicate that the methodology can achieve accurate motor assessment (related to ground-truth UPDRS) for both “Off” and “On” stages.

关键词： Performance evaluation Signal processing algorithms Signal processing Sensors data mining Standards Diseases

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